JavaScript Azure Functions using OpenAI extension for OpenAI retrieval augmented generation with Azure AI Search
Using Azure Functions OpenAI trigger and bindings extension to import data and query with Azure Open AI and Azure AI Search
This sample contains a JavaScript Azure Function using OpenAI bindings extension to highlight OpenAI retrieval augmented generation with Azure AI Search.
You can learn more about the OpenAI trigger and bindings extension in the GitHub documentation and in the Official OpenAI extension documentation
Prerequisites
- Node 20
- Azure Functions Core Tools v4.x
- Azure OpenAI resource
- Azure AI Search resource
- Azurite
- Azure Developer CLI to create Azure resources automatically - recommended
Prepare your local environment
Create Azure OpenAI and Azure AI Search resources for local and cloud dev-test
Once you have your Azure subscription, run the following in a new terminal window to create Azure OpenAI, Azure AI Search and other resources needed: You will be asked if you want to enable a virtual network that will lock down your OpenAI and AI Search services so they are only available from the deployed function app over private endpoints. To skip virtual network integration, select true. If you select networking, your local IP will be added to the OpenAI and AI Search services so you can debug locally.
azd init --template https://github.com/Azure-Samples/azure-functions-openai-aisearch-node
Mac/Linux:
chmod +x ./infra/scripts/*.sh
Windows:
set-executionpolicy remotesigned
Run the follow command to provision resoruces in Azure
azd provision
When you run azd provision
, you can view detailed progress of resource deployment in the Azure Portal.
If you don't run azd provision, you can create an OpenAI resource and an AI Search resource in the Azure portal to get your endpoints. After it deploys, click Go to resource and view the Endpoint value. You will also need to deploy a model, e.g. with name chat
with model gpt-35-turbo
and embeddings
with model text-embedding-3-small
Create local.settings.json (Should be in the same folder as host.json. Automatically created if you ran azd provision)
{
"IsEncrypted": false,
"Values": {
"AzureWebJobsStorage": "UseDevelopmentStorage=true",
"FUNCTIONS_WORKER_RUNTIME": "node",
"AZURE_OPENAI_ENDPOINT": "<paste from above>",
"CHAT_MODEL_DEPLOYMENT_NAME": "chat",
"AZURE_AISEARCH_ENDPOINT": "<paste from above>",
"EMBEDDING_MODEL_DEPLOYMENT_NAME": "embeddings",
"SYSTEM_PROMPT": "You must only use the provided documents to answer the question"
}
}
Permissions
Add your account (contoso.microsoft.com) with the following permissions to the Azure OpenAI and AI Search resources when testing locally.
If you used azd provision
this step is already done - your logged in user and your function's managed identity already have permissions granted.
- Cognitive Services OpenAI User (OpenAI resource)
- Azure Search Service Contributor (AI Search resource)
- Azure Search Index Data Contributor (AI Search resource)
Access to Azure OpenAI and Azure AI Search with virtual network integration
If you selected virtual network integration, access to Azure OpenAI and Azure AI Search is limited to the Azure Function app through private endpoints and cannot be reached from the internet. To allow testing from your local machine, you need to go to the networking tab in Azure OpenAI and Azure AI Search and add your client ip to the allowed list. If you used azd provision
this step is already done.
Run your app using Visual Studio Code
- Open the folder in a new terminal.
- Run the
code .
code command to open the project in Visual Studio Code. - In the command palette (F1), type
Azurite: Start
, which enables debugging without warnings. - Press Run/Debug (F5) to run in the debugger. Select Debug anyway if prompted about local emulator not running.
- Send POST requests to the
IngestFile
andPromptFile
endpoints respectively using your HTTP test tool. If you have the RestClient extension installed, you can execute requests directly from thetest.http
project file.
Deploy to Azure
Run this command to provision the function app, with any required Azure resources, and deploy your code:
azd up
You're prompted to supply these required deployment parameters:
Parameter | Description |
---|---|
Environment name | An environment that's used to maintain a unique deployment context for your app. You won't be prompted if you created the local project using azd init . |
Azure subscription | Subscription in which your resources are created. |
Azure location | Azure region in which to create the resource group that contains the new Azure resources. Only regions that currently support the Flex Consumption plan are shown. |
After publish completes successfully, azd
provides you with the URL endpoints of your new functions, but without the function key values required to access the endpoints. To learn how to obtain these same endpoints along with the required function keys, see Invoke the function on Azure in the companion article Quickstart: Create and deploy functions to Azure Functions using the Azure Developer CLI.
Redeploy your code
You can run the azd up
command as many times as you need to both provision your Azure resources and deploy code updates to your function app.
Huomautus
Deployed code files are always overwritten by the latest deployment package.
Clean up resources
When you're done working with your function app and related resources, you can use this command to delete the function app and its related resources from Azure and avoid incurring any further costs (--purge does not leave a soft delete of AI resource and recovers your quota):
azd down --purge